title: Semantic characteristics prediction of pulmonary nodule using Artificial Neural Networks.
abstract: Since it is difficult to choose which computer calculated features are effective to predict the malignancy of pulmonary nodules, in this study, we add a semantic-level of Artificial Neural Networks (ANNs) structure to improve intuition of features selection. The works of this study include two: 1) seeking the relationships between computer-calculated features and medical semantic concepts which could be understood by human; 2) providing an objective assessment method to predict the malignancy from semantic characteristics. We used 60 thoracic CT scans collected from the Lung Image Database Consortium (LIDC) database, in which the suspicious lesions had been delineated and annotated by 4 radiologists independently. Corresponding to the two works of this study, correlation analysis experiment and agreement experiment were performed separately.
authors.forename: Guangxu
authors.initials: G
authors.lastname: Li
authors.forename: Hyoungseop
authors.initials: H
authors.lastname: Kim
authors.forename: Joo Kooi
authors.initials: JK
authors.lastname: Tan
authors.forename: Seiji
authors.initials: S
authors.lastname: Ishikawa
authors.forename: Yasushi
authors.initials: Y
authors.lastname: Hirano
authors.forename: Shoji
authors.initials: S
authors.lastname: Kido
authors.forename: Rie
authors.initials: R
authors.lastname: Tachibana
mesh_terms: D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008168:Lung / Q000473:pathology; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D016571:Neural Networks, Computer; D011859:Radiography / Q000379:methods; D012660:Semantics; D009626:Terminology as Topic; D014057:Tomography, X-Ray Computed / Q000379:methods